﻿/* 
 * Copyright (c) 1994 Anthony Dekker
 * Ported to Java by Kevin Weiner, FM Software
 *
 * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
 * See "Kohonen neural networks for optimal colour quantization"
 * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
 * for a discussion of the algorithm.
 *
 * Any party obtaining a copy of these files from the author, directly or
 * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
 * world-wide, paid up, royalty-free, nonexclusive right and license to deal
 * in this software and documentation files (the "Software"), including without
 * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons who receive
 * copies from any such party to do so, with the only requirement being
 * that this copyright notice remain intact.
 */

using System;

namespace Moments.Encoder
{
	public class NeuQuant
	{
		protected static readonly int netsize = 256; // Number of colours used

		// Four primes near 500 - assume no image has a length so large that it is divisible by all four primes
		protected static readonly int prime1 = 499;
		protected static readonly int prime2 = 491;
		protected static readonly int prime3 = 487;
		protected static readonly int prime4 = 503;

		protected static readonly int minpicturebytes = (3 * prime4); // Minimum size for input image

		// Network Definitions
		protected static readonly int maxnetpos = (netsize - 1);
		protected static readonly int netbiasshift = 4; // Bias for colour values
		protected static readonly int ncycles = 100; // No. of learning cycles

		// Defs for freq and bias
		protected static readonly int intbiasshift = 16; // Bias for fractions
		protected static readonly int intbias = (((int)1) << intbiasshift);
		protected static readonly int gammashift = 10; // Gamma = 1024
		protected static readonly int gamma = (((int)1) << gammashift);
		protected static readonly int betashift = 10;
		protected static readonly int beta = (intbias >> betashift); // Beta = 1/1024
		protected static readonly int betagamma = (intbias << (gammashift - betashift));

		// Defs for decreasing radius factor
		protected static readonly int initrad = (netsize >> 3); // For 256 cols, radius starts
		protected static readonly int radiusbiasshift = 6; // At 32.0 biased by 6 bits
		protected static readonly int radiusbias = (((int)1) << radiusbiasshift);
		protected static readonly int initradius = (initrad * radiusbias); // And decreases by a
		protected static readonly int radiusdec = 30; // Factor of 1/30 each cycle

		// Defs for decreasing alpha factor
		protected static readonly int alphabiasshift = 10; /* alpha starts at 1.0 */
		protected static readonly int initalpha = (((int)1) << alphabiasshift);

		protected int alphadec; // Biased by 10 bits

		// Radbias and alpharadbias used for radpower calculation
		protected static readonly int radbiasshift = 8;
		protected static readonly int radbias = (((int)1) << radbiasshift);
		protected static readonly int alpharadbshift = (alphabiasshift + radbiasshift);
		protected static readonly int alpharadbias = (((int)1) << alpharadbshift);

		// Types and Global Variables
		protected byte[] thepicture; // The input image itself
		protected int lengthcount; // Lengthcount = H*W*3
		protected int samplefac; // Sampling factor 1..30
		protected int[][] network; // The network itself - [netsize][4]
		protected int[] netindex = new int[256]; // For network lookup - really 256
		protected int[] bias = new int[netsize]; // Bias and freq arrays for learning
		protected int[] freq = new int[netsize];
		protected int[] radpower = new int[initrad]; // Radpower for precomputation

		// Initialize network in range (0,0,0) to (255,255,255) and set parameters
		public NeuQuant(byte[] thepic, int len, int sample)
		{
			int i;
			int[] p;

			thepicture = thepic;
			lengthcount = len;
			samplefac = sample;

			network = new int[netsize][];
			for (i = 0; i < netsize; i++)
			{
				network[i] = new int[4];
				p = network[i];
				p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
				freq[i] = intbias / netsize; // 1 / netsize
				bias[i] = 0;
			}
		}

		public byte[] ColorMap()
		{
			byte[] map = new byte[3 * netsize];
			int[] index = new int[netsize];

			for (int i = 0; i < netsize; i++)
				index[network[i][3]] = i;

			int k = 0;
			for (int i = 0; i < netsize; i++)
			{
				int j = index[i];
				map[k++] = (byte)(network[j][0]);
				map[k++] = (byte)(network[j][1]);
				map[k++] = (byte)(network[j][2]);
			}

			return map;
		}

		// Insertion sort of network and building of netindex[0..255] (to do after unbias)
		public void Inxbuild()
		{
			int i, j, smallpos, smallval;
			int[] p;
			int[] q;
			int previouscol, startpos;

			previouscol = 0;
			startpos = 0;

			for (i = 0; i < netsize; i++)
			{
				p = network[i];
				smallpos = i;
				smallval = p[1]; // Index on g

				// Find smallest in i..netsize-1
				for (j = i + 1; j < netsize; j++)
				{
					q = network[j];
					if (q[1] < smallval)
					{
						smallpos = j;
						smallval = q[1]; // Index on g
					}
				}

				q = network[smallpos];

				// Swap p (i) and q (smallpos) entries
				if (i != smallpos)
				{
					j = q[0];
					q[0] = p[0];
					p[0] = j;
					j = q[1];
					q[1] = p[1];
					p[1] = j;
					j = q[2];
					q[2] = p[2];
					p[2] = j;
					j = q[3];
					q[3] = p[3];
					p[3] = j;
				}

				// Smallval entry is now in position i
				if (smallval != previouscol)
				{
					netindex[previouscol] = (startpos + i) >> 1;

					for (j = previouscol + 1; j < smallval; j++)
						netindex[j] = i;

					previouscol = smallval;
					startpos = i;
				}
			}

			netindex[previouscol] = (startpos + maxnetpos) >> 1;

			for (j = previouscol + 1; j < 256; j++)
				netindex[j] = maxnetpos;
		}

		// Main Learning Loop
		public void Learn()
		{
			int i, j, b, g, r;
			int radius, rad, alpha, step, delta, samplepixels;
			byte[] p;
			int pix, lim;

			if (lengthcount < minpicturebytes)
				samplefac = 1;

			alphadec = 30 + ((samplefac - 1) / 3);
			p = thepicture;
			pix = 0;
			lim = lengthcount;
			samplepixels = lengthcount / (3 * samplefac);
			delta = samplepixels / ncycles;
			alpha = initalpha;
			radius = initradius;

			rad = radius >> radiusbiasshift;

			if (rad <= 1)
				rad = 0;

			for (i = 0; i < rad; i++)
				radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));

			if (lengthcount < minpicturebytes)
			{
				step = 3;
			}
			else if ((lengthcount % prime1) != 0)
			{
				step = 3 * prime1;
			}
			else
			{
				if ((lengthcount % prime2) != 0)
				{
					step = 3 * prime2;
				}
				else
				{
					if ((lengthcount % prime3) != 0)
						step = 3 * prime3;
					else
						step = 3 * prime4;
				}
			}

			i = 0;
			while (i < samplepixels)
			{
				b = (p[pix + 0] & 0xff) << netbiasshift;
				g = (p[pix + 1] & 0xff) << netbiasshift;
				r = (p[pix + 2] & 0xff) << netbiasshift;
				j = Contest(b, g, r);

				Altersingle(alpha, j, b, g, r);

				if (rad != 0)
					Alterneigh(rad, j, b, g, r); // Alter neighbours

				pix += step;

				if (pix >= lim)
					pix -= lengthcount;

				i++;

				if (delta == 0)
					delta = 1;

				if (i % delta == 0)
				{
					alpha -= alpha / alphadec;
					radius -= radius / radiusdec;
					rad = radius >> radiusbiasshift;

					if (rad <= 1)
						rad = 0;

					for (j = 0; j < rad; j++)
						radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
				}
			}
		}

		// Search for BGR values 0..255 (after net is unbiased) and return colour index
		public int Map(int b, int g, int r)
		{
			int i, j, dist, a, bestd;
			int[] p;
			int best;

			bestd = 1000; // Biggest possible dist is 256*3
			best = -1;
			i = netindex[g]; // Index on g
			j = i - 1; // Start at netindex[g] and work outwards

			while ((i < netsize) || (j >= 0))
			{
				if (i < netsize)
				{
					p = network[i];
					dist = p[1] - g; // Inx key

					if (dist >= bestd)
					{
						i = netsize; // Stop iter
					}
					else
					{
						i++;

						if (dist < 0)
							dist = -dist;

						a = p[0] - b;

						if (a < 0)
							a = -a;

						dist += a;

						if (dist < bestd)
						{
							a = p[2] - r;

							if (a < 0)
								a = -a;

							dist += a;

							if (dist < bestd)
							{
								bestd = dist;
								best = p[3];
							}
						}
					}
				}

				if (j >= 0)
				{
					p = network[j];
					dist = g - p[1]; // Inx key - reverse dif

					if (dist >= bestd)
					{
						j = -1; // Stop iter
					}
					else
					{
						j--;

						if (dist < 0)
							dist = -dist;

						a = p[0] - b;

						if (a < 0)
							a = -a;

						dist += a;

						if (dist < bestd)
						{
							a = p[2] - r;

							if (a < 0)
								a = -a;

							dist += a;

							if (dist < bestd)
							{
								bestd = dist;
								best = p[3];
							}
						}
					}
				}
			}

			return best;
		}

		public byte[] Process()
		{
			Learn();
			Unbiasnet();
			Inxbuild();
			return ColorMap();
		}

		// Unbias network to give byte values 0..255 and record position i to prepare for sort
		public void Unbiasnet()
		{
			int i;

			for (i = 0; i < netsize; i++)
			{
				network[i][0] >>= netbiasshift;
				network[i][1] >>= netbiasshift;
				network[i][2] >>= netbiasshift;
				network[i][3] = i; // Record colour no
			}
		}

		// Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
		protected void Alterneigh(int rad, int i, int b, int g, int r)
		{
			int j, k, lo, hi, a, m;
			int[] p;

			lo = i - rad;

			if (lo < -1)
				lo = -1;

			hi = i + rad;

			if (hi > netsize)
				hi = netsize;

			j = i + 1;
			k = i - 1;
			m = 1;

			while ((j < hi) || (k > lo))
			{
				a = radpower[m++];

				if (j < hi)
				{
					p = network[j++];
					p[0] -= (a * (p[0] - b)) / alpharadbias;
					p[1] -= (a * (p[1] - g)) / alpharadbias;
					p[2] -= (a * (p[2] - r)) / alpharadbias;
				}

				if (k > lo)
				{
					p = network[k--];
					p[0] -= (a * (p[0] - b)) / alpharadbias;
					p[1] -= (a * (p[1] - g)) / alpharadbias;
					p[2] -= (a * (p[2] - r)) / alpharadbias;
				}
			}
		}

		// Move neuron i towards biased (b,g,r) by factor alpha
		protected void Altersingle(int alpha, int i, int b, int g, int r)
		{
			/* Alter hit neuron */
			int[] n = network[i];
			n[0] -= (alpha * (n[0] - b)) / initalpha;
			n[1] -= (alpha * (n[1] - g)) / initalpha;
			n[2] -= (alpha * (n[2] - r)) / initalpha;
		}

		// Search for biased BGR values
		protected int Contest(int b, int g, int r)
		{
			// Finds closest neuron (min dist) and updates freq
			// Finds best neuron (min dist-bias) and returns position
			// For frequently chosen neurons, freq[i] is high and bias[i] is negative
			// bias[i] = gamma*((1/netsize)-freq[i])

			int i, dist, a, biasdist, betafreq;
			int bestpos, bestbiaspos, bestd, bestbiasd;
			int[] n;

			bestd = ~(((int)1) << 31);
			bestbiasd = bestd;
			bestpos = -1;
			bestbiaspos = bestpos;

			for (i = 0; i < netsize; i++)
			{
				n = network[i];
				dist = n[0] - b;

				if (dist < 0)
					dist = -dist;

				a = n[1] - g;

				if (a < 0)
					a = -a;

				dist += a;
				a = n[2] - r;

				if (a < 0)
					a = -a;

				dist += a;

				if (dist < bestd)
				{
					bestd = dist;
					bestpos = i;
				}

				biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));

				if (biasdist < bestbiasd)
				{
					bestbiasd = biasdist;
					bestbiaspos = i;
				}

				betafreq = (freq[i] >> betashift);
				freq[i] -= betafreq;
				bias[i] += (betafreq << gammashift);
			}

			freq[bestpos] += beta;
			bias[bestpos] -= betagamma;
			return bestbiaspos;
		}
	}
}
